A robust GWSS method to simultaneously detect rare and common variants for complex disease

Abstract

The rapid advances in sequencing technologies and the resulting next-generation sequencing data provide the opportunity to detect disease-associated variants with a better solution, in particular for low-frequency variants. Although both common and rare variants might exert their independent effects on the risk for the trait of interest, previous methods to detect the association effects rarely consider them simultaneously. We proposed a class of test statistics, the generalized weighted-sum statistic (GWSS), to detect disease associations in the presence of common and rare variants with a case-control study design. Information of rare variants was aggregated using a weighted sum method, while signal directions and strength of the variants were considered at the same time. Permutations were performed to obtain the empirical p-values of the test statistics. Our simulation showed that, compared to the existing methods, the GWSS method had better performance in most of the scenarios. The GWSS (in particular VDWSS-t) method is particularly robust for opposite association directions, association strength, and varying distributions of minor-allele frequencies. It is therefore promising for detecting disease-associated loci. For empirical data application, we also applied our GWSS method to the Genetic Analysis Workshop 17 data, and the results were consistent with the simulation, suggesting good performance of our method. As re-sequencing studies become more popular to identify putative disease loci, we recommend the use of this newly developed GWSS to detect associations with both common and rare variants.

Fig 1. Detection power (ρ = 0) for different MAF distributions of signal and noise rare variants.

The left panel considers OR_j = 2 for rare variants (RVs) and 1.5 for common variant (CV), and the right panel considers OR_j = 1/2 for RVs and 1/1.5 for CV. Each panel considers four scenarios: scenario1 considers 8 signal RVs and 8 noise RVs; scenario2 considers 8 signal RVs, 4 noise RVs and 4 noise CVs; scenario3 considers 7 signal RVs, 1 signal CV and 8 noise RVs; and scenario4 considers 7 signal RVs, 1 signal CV, 4 noise RVs and 4 noise CVs. The results were based on 1000 subjects (500 cases and 500 controls). All empirical p-values were calculated from 500 permutations. The detection power is defined as the proportion of test statistic attained significant level 0.05 over 1000 simulations. CMC method use 0.01 as a threshold for rare variants. SKAT₁ and SKAT_b represents SKAT using equal weight and using beta(1,25) density function evaluated at q _j as the weight, respectively. SKAT-C and SKAT-A combined SKAT and sum test and adaptive sum test, respectively.